Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations90767
Missing cells13714
Missing cells (%)0.7%
Total size in memory15.2 MiB
Average record size in memory176.0 B

Variable types

Numeric11
Text11

Alerts

merch_zipcode has 13714 (15.1%) missing values Missing
amt is highly skewed (γ1 = 26.29357902) Skewed
trans_num has unique values Unique
is_fraud has 90242 (99.4%) zeros Zeros

Reproduction

Analysis started2025-06-23 17:29:36.620836
Analysis finished2025-06-23 17:29:37.623140
Duration1 second
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

cc_num
Real number (ℝ)

Distinct949
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.217238762 × 1017
Minimum6.041620718 × 1010
Maximum4.992346398 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:41.164629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.041620718 × 1010
5-th percentile6.304848798 × 1011
Q11.800400275 × 1014
median3.520550088 × 1015
Q34.651007078 × 1015
95-th percentile4.502539527 × 1018
Maximum4.992346398 × 1018
Range4.992346338 × 1018
Interquartile range (IQR)4.47096705 × 1015

Descriptive statistics

Standard deviation1.315095899 × 1018
Coefficient of variation (CV)3.118381418
Kurtosis6.062046089
Mean4.217238762 × 1017
Median Absolute Deviation (MAD)3.140652844 × 1015
Skewness2.83104677
Sum1.617115973 × 1018
Variance1.729477223 × 1036
MonotonicityNot monotonic
2025-06-23T18:29:41.245071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.011893665 × 1015249
 
0.3%
3.725092582 × 1014246
 
0.3%
4.716561797 × 1015241
 
0.3%
3.02387559 × 1013240
 
0.3%
6.011109737 × 1015235
 
0.3%
4.364010865 × 1015233
 
0.3%
6.534628261 × 1015233
 
0.3%
6.011438889 × 1015232
 
0.3%
3.764452668 × 1014232
 
0.3%
3.521417321 × 1015231
 
0.3%
Other values (939) 88395
97.4%
ValueCountFrequency (%)
6.041620718 × 1010100
0.1%
6.042292873 × 101096
0.1%
6.042309813 × 101050
0.1%
6.042785159 × 101041
< 0.1%
6.048700208 × 101036
 
< 0.1%
ValueCountFrequency (%)
4.992346398 × 1018156
0.2%
4.989847571 × 101871
0.1%
4.980323468 × 101844
 
< 0.1%
4.973530368 × 101879
0.1%
4.958589672 × 1018113
0.1%
Distinct693
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:41.400816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length36
Mean length23.10297795
Min length13

Characters and Unicode

Total characters2096988
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Kerluke Inc
2nd rowfraud_Rempel PLC
3rd rowfraud_Rodriguez Group
4th rowfraud_Doyle Ltd
5th rowfraud_Leffler-Goldner
ValueCountFrequency (%)
and 33057
 
15.6%
llc 6829
 
3.2%
inc 6450
 
3.1%
sons 5124
 
2.4%
ltd 5043
 
2.4%
plc 4625
 
2.2%
group 3512
 
1.7%
fraud_kutch 698
 
0.3%
fraud_schaefer 679
 
0.3%
fraud_streich 676
 
0.3%
Other values (804) 144580
68.4%
2025-06-23T18:29:41.699851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 203486
 
9.7%
r 188491
 
9.0%
d 149640
 
7.1%
e 130340
 
6.2%
u 129837
 
6.2%
n 123982
 
5.9%
120506
 
5.7%
f 97747
 
4.7%
_ 90767
 
4.3%
o 79079
 
3.8%
Other values (45) 783113
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2096988
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 203486
 
9.7%
r 188491
 
9.0%
d 149640
 
7.1%
e 130340
 
6.2%
u 129837
 
6.2%
n 123982
 
5.9%
120506
 
5.7%
f 97747
 
4.7%
_ 90767
 
4.3%
o 79079
 
3.8%
Other values (45) 783113
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2096988
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 203486
 
9.7%
r 188491
 
9.0%
d 149640
 
7.1%
e 130340
 
6.2%
u 129837
 
6.2%
n 123982
 
5.9%
120506
 
5.7%
f 97747
 
4.7%
_ 90767
 
4.3%
o 79079
 
3.8%
Other values (45) 783113
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2096988
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 203486
 
9.7%
r 188491
 
9.0%
d 149640
 
7.1%
e 130340
 
6.2%
u 129837
 
6.2%
n 123982
 
5.9%
120506
 
5.7%
f 97747
 
4.7%
_ 90767
 
4.3%
o 79079
 
3.8%
Other values (45) 783113
37.3%
Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:41.825482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length10.51940683
Min length4

Characters and Unicode

Total characters954815
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmisc_net
2nd rowgrocery_net
3rd rowgas_transport
4th rowgrocery_pos
5th rowpersonal_care
ValueCountFrequency (%)
gas_transport 9236
10.2%
home 8710
9.6%
grocery_pos 8542
9.4%
shopping_pos 8057
8.9%
kids_pets 7905
8.7%
shopping_net 6714
7.4%
entertainment 6602
7.3%
personal_care 6450
7.1%
food_dining 6417
 
7.1%
health_fitness 6044
 
6.7%
Other values (4) 16090
17.7%
2025-06-23T18:29:41.929996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 99729
10.4%
e 90443
9.5%
o 85904
9.0%
n 83545
8.7%
t 75538
 
7.9%
p 75269
 
7.9%
_ 72592
 
7.6%
r 64371
 
6.7%
i 58158
 
6.1%
a 46881
 
4.9%
Other values (10) 202385
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 954815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 99729
10.4%
e 90443
9.5%
o 85904
9.0%
n 83545
8.7%
t 75538
 
7.9%
p 75269
 
7.9%
_ 72592
 
7.6%
r 64371
 
6.7%
i 58158
 
6.1%
a 46881
 
4.9%
Other values (10) 202385
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 954815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 99729
10.4%
e 90443
9.5%
o 85904
9.0%
n 83545
8.7%
t 75538
 
7.9%
p 75269
 
7.9%
_ 72592
 
7.6%
r 64371
 
6.7%
i 58158
 
6.1%
a 46881
 
4.9%
Other values (10) 202385
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 954815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 99729
10.4%
e 90443
9.5%
o 85904
9.0%
n 83545
8.7%
t 75538
 
7.9%
p 75269
 
7.9%
_ 72592
 
7.6%
r 64371
 
6.7%
i 58158
 
6.1%
a 46881
 
4.9%
Other values (10) 202385
21.2%

amt
Real number (ℝ)

Skewed 

Distinct20055
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.93903577
Minimum1
Maximum13536.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:41.986949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.42
Q19.63
median47.28
Q382.59
95-th percentile193.945
Maximum13536.84
Range13535.84
Interquartile range (IQR)72.96

Descriptive statistics

Standard deviation153.3540508
Coefficient of variation (CV)2.192681799
Kurtosis1427.651904
Mean69.93903577
Median Absolute Deviation (MAD)37.26
Skewness26.29357902
Sum6348156.46
Variance23517.46488
MonotonicityNot monotonic
2025-06-23T18:29:42.042416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 56
 
0.1%
1.25 55
 
0.1%
1.54 48
 
0.1%
1.64 47
 
0.1%
2.43 46
 
0.1%
1.27 46
 
0.1%
1.09 46
 
0.1%
1.24 43
 
< 0.1%
1.18 42
 
< 0.1%
1.4 42
 
< 0.1%
Other values (20045) 90296
99.5%
ValueCountFrequency (%)
1 14
 
< 0.1%
1.01 37
< 0.1%
1.02 31
< 0.1%
1.03 35
< 0.1%
1.04 38
< 0.1%
ValueCountFrequency (%)
13536.84 1
< 0.1%
9999.39 1
< 0.1%
8981.87 1
< 0.1%
8221.84 1
< 0.1%
8217.23 1
< 0.1%

first
Text

Distinct347
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:42.185397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.077329867
Min length3

Characters and Unicode

Total characters551621
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowMelody
2nd rowMichael
3rd rowKatelyn
4th rowDavid
5th rowEmily
ValueCountFrequency (%)
christopher 1867
 
2.1%
jessica 1467
 
1.6%
robert 1451
 
1.6%
david 1401
 
1.5%
michael 1376
 
1.5%
james 1352
 
1.5%
john 1166
 
1.3%
jennifer 1143
 
1.3%
mary 1133
 
1.2%
william 1118
 
1.2%
Other values (337) 77293
85.2%
2025-06-23T18:29:42.340017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 70063
 
12.7%
e 60389
 
10.9%
n 43345
 
7.9%
i 43227
 
7.8%
r 42213
 
7.7%
l 26939
 
4.9%
h 23991
 
4.3%
s 22758
 
4.1%
t 21674
 
3.9%
o 18961
 
3.4%
Other values (39) 178061
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 551621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 70063
 
12.7%
e 60389
 
10.9%
n 43345
 
7.9%
i 43227
 
7.8%
r 42213
 
7.7%
l 26939
 
4.9%
h 23991
 
4.3%
s 22758
 
4.1%
t 21674
 
3.9%
o 18961
 
3.4%
Other values (39) 178061
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 551621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 70063
 
12.7%
e 60389
 
10.9%
n 43345
 
7.9%
i 43227
 
7.8%
r 42213
 
7.7%
l 26939
 
4.9%
h 23991
 
4.3%
s 22758
 
4.1%
t 21674
 
3.9%
o 18961
 
3.4%
Other values (39) 178061
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 551621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 70063
 
12.7%
e 60389
 
10.9%
n 43345
 
7.9%
i 43227
 
7.8%
r 42213
 
7.7%
l 26939
 
4.9%
h 23991
 
4.3%
s 22758
 
4.1%
t 21674
 
3.9%
o 18961
 
3.4%
Other values (39) 178061
32.3%

last
Text

Distinct474
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:42.461113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.107825531
Min length2

Characters and Unicode

Total characters554389
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowThompson
2nd rowJohnson
3rd rowWise
4th rowEverett
5th rowHall
ValueCountFrequency (%)
smith 2010
 
2.2%
williams 1611
 
1.8%
davis 1528
 
1.7%
johnson 1408
 
1.6%
rodriguez 1184
 
1.3%
martinez 1085
 
1.2%
jones 970
 
1.1%
lewis 877
 
1.0%
gonzalez 877
 
1.0%
martin 808
 
0.9%
Other values (464) 78409
86.4%
2025-06-23T18:29:42.603624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 54832
 
9.9%
r 46276
 
8.3%
a 45735
 
8.2%
n 42791
 
7.7%
o 40707
 
7.3%
s 33969
 
6.1%
l 33894
 
6.1%
i 30378
 
5.5%
t 20377
 
3.7%
h 15909
 
2.9%
Other values (38) 189521
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 554389
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 54832
 
9.9%
r 46276
 
8.3%
a 45735
 
8.2%
n 42791
 
7.7%
o 40707
 
7.3%
s 33969
 
6.1%
l 33894
 
6.1%
i 30378
 
5.5%
t 20377
 
3.7%
h 15909
 
2.9%
Other values (38) 189521
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 554389
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 54832
 
9.9%
r 46276
 
8.3%
a 45735
 
8.2%
n 42791
 
7.7%
o 40707
 
7.3%
s 33969
 
6.1%
l 33894
 
6.1%
i 30378
 
5.5%
t 20377
 
3.7%
h 15909
 
2.9%
Other values (38) 189521
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 554389
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 54832
 
9.9%
r 46276
 
8.3%
a 45735
 
8.2%
n 42791
 
7.7%
o 40707
 
7.3%
s 33969
 
6.1%
l 33894
 
6.1%
i 30378
 
5.5%
t 20377
 
3.7%
h 15909
 
2.9%
Other values (38) 189521
34.2%

gender
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:42.623808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters90767
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowM
5th rowF
ValueCountFrequency (%)
f 49491
54.5%
m 41276
45.5%
2025-06-23T18:29:42.660315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 49491
54.5%
M 41276
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 49491
54.5%
M 41276
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 49491
54.5%
M 41276
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 49491
54.5%
M 41276
45.5%

street
Text

Distinct949
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:42.765621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length29
Mean length22.21335948
Min length12

Characters and Unicode

Total characters2016240
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st row0362 Anderson Wall
2nd row094 Owens Underpass
3rd row674 Maureen Summit Apt. 276
4th row4138 David Fall
5th row8851 Reese Neck
ValueCountFrequency (%)
apt 23051
 
6.4%
suite 21237
 
5.9%
island 1612
 
0.4%
michael 1319
 
0.4%
common 1246
 
0.3%
station 1234
 
0.3%
islands 1205
 
0.3%
fields 1196
 
0.3%
brooks 1178
 
0.3%
david 1168
 
0.3%
Other values (1889) 306431
84.9%
2025-06-23T18:29:42.904892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
270110
 
13.4%
e 125698
 
6.2%
a 101371
 
5.0%
i 90327
 
4.5%
t 86943
 
4.3%
r 76902
 
3.8%
n 74611
 
3.7%
s 72408
 
3.6%
l 62403
 
3.1%
o 61074
 
3.0%
Other values (52) 994393
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
270110
 
13.4%
e 125698
 
6.2%
a 101371
 
5.0%
i 90327
 
4.5%
t 86943
 
4.3%
r 76902
 
3.8%
n 74611
 
3.7%
s 72408
 
3.6%
l 62403
 
3.1%
o 61074
 
3.0%
Other values (52) 994393
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
270110
 
13.4%
e 125698
 
6.2%
a 101371
 
5.0%
i 90327
 
4.5%
t 86943
 
4.3%
r 76902
 
3.8%
n 74611
 
3.7%
s 72408
 
3.6%
l 62403
 
3.1%
o 61074
 
3.0%
Other values (52) 994393
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
270110
 
13.4%
e 125698
 
6.2%
a 101371
 
5.0%
i 90327
 
4.5%
t 86943
 
4.3%
r 76902
 
3.8%
n 74611
 
3.7%
s 72408
 
3.6%
l 62403
 
3.1%
o 61074
 
3.0%
Other values (52) 994393
49.3%

city
Text

Distinct870
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:42.992023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.653177917
Min length3

Characters and Unicode

Total characters785423
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowMound City
2nd rowNorwalk
3rd rowScotts Mills
4th rowMorrisdale
5th rowBasye
ValueCountFrequency (%)
city 1496
 
1.3%
west 1381
 
1.2%
saint 1006
 
0.9%
north 988
 
0.9%
falls 913
 
0.8%
mount 808
 
0.7%
new 794
 
0.7%
lake 769
 
0.7%
san 712
 
0.6%
springs 607
 
0.5%
Other values (898) 103526
91.6%
2025-06-23T18:29:43.293132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 76376
 
9.7%
a 65037
 
8.3%
n 57989
 
7.4%
o 57205
 
7.3%
l 54773
 
7.0%
r 52418
 
6.7%
i 49399
 
6.3%
t 42305
 
5.4%
s 31107
 
4.0%
22233
 
2.8%
Other values (42) 276581
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 785423
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 76376
 
9.7%
a 65037
 
8.3%
n 57989
 
7.4%
o 57205
 
7.3%
l 54773
 
7.0%
r 52418
 
6.7%
i 49399
 
6.3%
t 42305
 
5.4%
s 31107
 
4.0%
22233
 
2.8%
Other values (42) 276581
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 785423
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 76376
 
9.7%
a 65037
 
8.3%
n 57989
 
7.4%
o 57205
 
7.3%
l 54773
 
7.0%
r 52418
 
6.7%
i 49399
 
6.3%
t 42305
 
5.4%
s 31107
 
4.0%
22233
 
2.8%
Other values (42) 276581
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 785423
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 76376
 
9.7%
a 65037
 
8.3%
n 57989
 
7.4%
o 57205
 
7.3%
l 54773
 
7.0%
r 52418
 
6.7%
i 49399
 
6.3%
t 42305
 
5.4%
s 31107
 
4.0%
22233
 
2.8%
Other values (42) 276581
35.2%

state
Text

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:43.355935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters181534
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMO
2nd rowCA
3rd rowOR
4th rowPA
5th rowVA
ValueCountFrequency (%)
tx 6531
 
7.2%
ny 5819
 
6.4%
pa 5693
 
6.3%
ca 3961
 
4.4%
oh 3350
 
3.7%
mi 3264
 
3.6%
al 2993
 
3.3%
il 2950
 
3.3%
fl 2923
 
3.2%
mo 2637
 
2.9%
Other values (41) 50646
55.8%
2025-06-23T18:29:43.441221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 25156
13.9%
N 19991
 
11.0%
M 15517
 
8.5%
I 12625
 
7.0%
T 10758
 
5.9%
L 10342
 
5.7%
O 10162
 
5.6%
C 9770
 
5.4%
Y 9049
 
5.0%
X 6531
 
3.6%
Other values (14) 51633
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 181534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 25156
13.9%
N 19991
 
11.0%
M 15517
 
8.5%
I 12625
 
7.0%
T 10758
 
5.9%
L 10342
 
5.7%
O 10162
 
5.6%
C 9770
 
5.4%
Y 9049
 
5.0%
X 6531
 
3.6%
Other values (14) 51633
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 181534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 25156
13.9%
N 19991
 
11.0%
M 15517
 
8.5%
I 12625
 
7.0%
T 10758
 
5.9%
L 10342
 
5.7%
O 10162
 
5.6%
C 9770
 
5.4%
Y 9049
 
5.0%
X 6531
 
3.6%
Other values (14) 51633
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 181534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 25156
13.9%
N 19991
 
11.0%
M 15517
 
8.5%
I 12625
 
7.0%
T 10758
 
5.9%
L 10342
 
5.7%
O 10162
 
5.6%
C 9770
 
5.4%
Y 9049
 
5.0%
X 6531
 
3.6%
Other values (14) 51633
28.4%

zip
Real number (ℝ)

Distinct938
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48727.78369
Minimum1257
Maximum99783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:43.475514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7208
Q126041
median48088
Q372042
95-th percentile94619
Maximum99783
Range98526
Interquartile range (IQR)46001

Descriptive statistics

Standard deviation26940.08393
Coefficient of variation (CV)0.5528690593
Kurtosis-1.096946247
Mean48727.78369
Median Absolute Deviation (MAD)23105
Skewness0.08430307804
Sum4422874742
Variance725768122.2
MonotonicityNot monotonic
2025-06-23T18:29:43.511497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48088 276
 
0.3%
34112 250
 
0.3%
80120 249
 
0.3%
48438 246
 
0.3%
73754 241
 
0.3%
59448 241
 
0.3%
76578 240
 
0.3%
28405 235
 
0.3%
5461 233
 
0.3%
89512 233
 
0.3%
Other values (928) 88323
97.3%
ValueCountFrequency (%)
1257 140
0.2%
1330 69
0.1%
1535 33
 
< 0.1%
1545 60
0.1%
1612 43
 
< 0.1%
ValueCountFrequency (%)
99783 116
0.1%
99747 1
 
< 0.1%
99746 33
 
< 0.1%
99323 191
0.2%
99160 228
0.3%

lat
Real number (ℝ)

Distinct936
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.57377767
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:43.545452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.8872
Q134.6689
median39.4055
Q342.0144
95-th percentile45.8433
Maximum66.6933
Range46.6662
Interquartile range (IQR)7.3455

Descriptive statistics

Standard deviation5.070830013
Coefficient of variation (CV)0.1314579571
Kurtosis0.8212984229
Mean38.57377767
Median Absolute Deviation (MAD)3.3811
Skewness-0.1871314491
Sum3501226.078
Variance25.71331702
MonotonicityNot monotonic
2025-06-23T18:29:43.584932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.5164 276
 
0.3%
26.1184 250
 
0.3%
39.5994 249
 
0.3%
42.9147 246
 
0.3%
36.385 241
 
0.3%
48.2777 241
 
0.3%
30.592 240
 
0.3%
34.2651 235
 
0.3%
44.3346 233
 
0.3%
39.5483 233
 
0.3%
Other values (926) 88323
97.3%
ValueCountFrequency (%)
20.0271 106
0.1%
20.0827 75
 
0.1%
24.6557 171
0.2%
26.1184 250
0.3%
26.3304 29
 
< 0.1%
ValueCountFrequency (%)
66.6933 1
 
< 0.1%
65.6899 33
 
< 0.1%
64.7556 116
0.1%
48.8878 228
0.3%
48.8856 150
0.2%

long
Real number (ℝ)

Distinct937
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.23810249
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative90767
Negative (%)100.0%
Memory size709.2 KiB
2025-06-23T18:29:43.629543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-119.7957
Q1-96.798
median-87.4581
Q3-80.1284
95-th percentile-73.5112
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.6696

Descriptive statistics

Standard deviation13.80436391
Coefficient of variation (CV)-0.1529771075
Kurtosis1.859241866
Mean-90.23810249
Median Absolute Deviation (MAD)8.1464
Skewness-1.153925254
Sum-8190641.849
Variance190.5604629
MonotonicityNot monotonic
2025-06-23T18:29:43.662884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-82.9832 276
 
0.3%
-81.7361 250
 
0.3%
-105.0044 249
 
0.3%
-83.4845 246
 
0.3%
-112.8456 241
 
0.3%
-98.0727 241
 
0.3%
-97.2893 240
 
0.3%
-77.867 235
 
0.3%
-73.098 233
 
0.3%
-119.7957 233
 
0.3%
Other values (927) 88323
97.3%
ValueCountFrequency (%)
-165.6723 116
0.1%
-156.292 33
 
< 0.1%
-155.488 75
0.1%
-155.3697 106
0.1%
-153.994 1
 
< 0.1%
ValueCountFrequency (%)
-67.9503 152
0.2%
-68.5565 69
0.1%
-69.2675 32
 
< 0.1%
-69.4828 136
0.1%
-69.9576 43
 
< 0.1%

city_pop
Real number (ℝ)

Distinct856
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88019.77275
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:43.696267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile139
Q1743
median2456
Q320328
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)19585

Descriptive statistics

Standard deviation298024.6363
Coefficient of variation (CV)3.385882819
Kurtosis37.62957174
Mean88019.77275
Median Absolute Deviation (MAD)2198
Skewness5.581723772
Sum7989290713
Variance8.881868384 × 1010
MonotonicityNot monotonic
2025-06-23T18:29:43.730223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1312922 388
 
0.4%
606 381
 
0.4%
1766 353
 
0.4%
1595797 352
 
0.4%
241 319
 
0.4%
198 315
 
0.3%
302 309
 
0.3%
910148 306
 
0.3%
2135 286
 
0.3%
276002 279
 
0.3%
Other values (846) 87479
96.4%
ValueCountFrequency (%)
23 132
0.1%
37 73
 
0.1%
43 150
0.2%
46 203
0.2%
47 43
 
< 0.1%
ValueCountFrequency (%)
2906700 268
0.3%
2504700 143
0.2%
2383912 36
 
< 0.1%
1595797 352
0.4%
1577385 160
0.2%

job
Text

Distinct486
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:43.814744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.22102747
Min length3

Characters and Unicode

Total characters1835402
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowArchitect
2nd rowFirefighter
3rd rowEngineer, petroleum
4th rowAdvice worker
5th rowEngineer, mining
ValueCountFrequency (%)
engineer 9169
 
4.5%
officer 7666
 
3.8%
manager 4311
 
2.1%
scientist 3900
 
1.9%
designer 3550
 
1.8%
surveyor 3344
 
1.7%
teacher 2685
 
1.3%
psychologist 2332
 
1.2%
research 2013
 
1.0%
editor 1995
 
1.0%
Other values (452) 160616
79.7%
2025-06-23T18:29:43.933707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 196159
 
10.7%
i 167019
 
9.1%
r 153990
 
8.4%
a 127805
 
7.0%
t 124373
 
6.8%
n 123357
 
6.7%
110814
 
6.0%
o 104130
 
5.7%
s 101392
 
5.5%
c 92566
 
5.0%
Other values (43) 533797
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1835402
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 196159
 
10.7%
i 167019
 
9.1%
r 153990
 
8.4%
a 127805
 
7.0%
t 124373
 
6.8%
n 123357
 
6.7%
110814
 
6.0%
o 104130
 
5.7%
s 101392
 
5.5%
c 92566
 
5.0%
Other values (43) 533797
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1835402
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 196159
 
10.7%
i 167019
 
9.1%
r 153990
 
8.4%
a 127805
 
7.0%
t 124373
 
6.8%
n 123357
 
6.7%
110814
 
6.0%
o 104130
 
5.7%
s 101392
 
5.5%
c 92566
 
5.0%
Other values (43) 533797
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1835402
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 196159
 
10.7%
i 167019
 
9.1%
r 153990
 
8.4%
a 127805
 
7.0%
t 124373
 
6.8%
n 123357
 
6.7%
110814
 
6.0%
o 104130
 
5.7%
s 101392
 
5.5%
c 92566
 
5.0%
Other values (43) 533797
29.1%

trans_num
Text

Unique 

Distinct90767
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:44.049583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2904544
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90767 ?
Unique (%)100.0%

Sample

1st row3d21bce7967838c3988cfe0f7fca878a
2nd rowfda7712b4bbcaab36afded37ab55047f
3rd row59161e0002642934974c1ae98bfa1f55
4th rowf487a7098c0bd4d45f710be1745c4acb
5th rowc2ed76f03cce8a6b362729a5a23f01c2
ValueCountFrequency (%)
3d21bce7967838c3988cfe0f7fca878a 1
 
< 0.1%
d047231d08ba22e60b6ff3b9fc0a50db 1
 
< 0.1%
c2ed76f03cce8a6b362729a5a23f01c2 1
 
< 0.1%
08afca2c21a05c8dfabfc2564d88ced6 1
 
< 0.1%
e0102a704a1c61b7a64d01dd72a2993d 1
 
< 0.1%
dc1fa86aba755a0f50097699fe6d44e9 1
 
< 0.1%
566250df239b92a1250d5b0ace559bf0 1
 
< 0.1%
ac2cd71d98e6cd1f3c98894977d28614 1
 
< 0.1%
c0bb681843a1c1b60739b909ad2955eb 1
 
< 0.1%
f47012152799267e90676dfdeb2641da 1
 
< 0.1%
Other values (90757) 90757
> 99.9%
2025-06-23T18:29:44.180704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 182302
 
6.3%
4 182129
 
6.3%
9 182090
 
6.3%
e 182023
 
6.3%
0 181972
 
6.3%
b 181802
 
6.3%
a 181494
 
6.2%
f 181432
 
6.2%
1 181406
 
6.2%
d 181396
 
6.2%
Other values (6) 1086498
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2904544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 182302
 
6.3%
4 182129
 
6.3%
9 182090
 
6.3%
e 182023
 
6.3%
0 181972
 
6.3%
b 181802
 
6.3%
a 181494
 
6.2%
f 181432
 
6.2%
1 181406
 
6.2%
d 181396
 
6.2%
Other values (6) 1086498
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2904544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 182302
 
6.3%
4 182129
 
6.3%
9 182090
 
6.3%
e 182023
 
6.3%
0 181972
 
6.3%
b 181802
 
6.3%
a 181494
 
6.2%
f 181432
 
6.2%
1 181406
 
6.2%
d 181396
 
6.2%
Other values (6) 1086498
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2904544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 182302
 
6.3%
4 182129
 
6.3%
9 182090
 
6.3%
e 182023
 
6.3%
0 181972
 
6.3%
b 181802
 
6.3%
a 181494
 
6.2%
f 181432
 
6.2%
1 181406
 
6.2%
d 181396
 
6.2%
Other values (6) 1086498
37.4%

merch_lat
Real number (ℝ)

Distinct90516
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.57366865
Minimum19.033288
Maximum66.624674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:44.212969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.033288
5-th percentile29.7800809
Q134.7524385
median39.415483
Q341.9808155
95-th percentile46.037913
Maximum66.624674
Range47.591386
Interquartile range (IQR)7.228377

Descriptive statistics

Standard deviation5.102019892
Coefficient of variation (CV)0.1322669082
Kurtosis0.8129226031
Mean38.57366865
Median Absolute Deviation (MAD)3.383273
Skewness-0.1818535362
Sum3501216.183
Variance26.03060698
MonotonicityNot monotonic
2025-06-23T18:29:44.243577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.531126 3
 
< 0.1%
33.600014 3
 
< 0.1%
34.484633 2
 
< 0.1%
35.866069 2
 
< 0.1%
41.491069 2
 
< 0.1%
43.108958 2
 
< 0.1%
39.162349 2
 
< 0.1%
39.451583 2
 
< 0.1%
38.663229 2
 
< 0.1%
38.305552 2
 
< 0.1%
Other values (90506) 90745
> 99.9%
ValueCountFrequency (%)
19.033288 1
< 0.1%
19.036618 1
< 0.1%
19.04188 1
< 0.1%
19.063792 1
< 0.1%
19.095712 1
< 0.1%
ValueCountFrequency (%)
66.624674 1
< 0.1%
66.554249 1
< 0.1%
66.514576 1
< 0.1%
66.454209 1
< 0.1%
66.436224 1
< 0.1%

merch_long
Real number (ℝ)

Distinct90682
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.23894977
Minimum-166.661968
Maximum-66.962913
Zeros0
Zeros (%)0.0%
Negative90767
Negative (%)100.0%
Memory size709.2 KiB
2025-06-23T18:29:44.277483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.661968
5-th percentile-119.4410536
Q1-96.882401
median-87.413904
Q3-80.2009085
95-th percentile-73.3614921
Maximum-66.962913
Range99.699055
Interquartile range (IQR)16.6814925

Descriptive statistics

Standard deviation13.8168318
Coefficient of variation (CV)-0.1531138365
Kurtosis1.848091919
Mean-90.23894977
Median Absolute Deviation (MAD)8.256166
Skewness-1.150196567
Sum-8190718.754
Variance190.904841
MonotonicityNot monotonic
2025-06-23T18:29:44.320950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-84.045893 2
 
< 0.1%
-96.477598 2
 
< 0.1%
-80.173837 2
 
< 0.1%
-85.324174 2
 
< 0.1%
-83.964422 2
 
< 0.1%
-97.536537 2
 
< 0.1%
-80.277138 2
 
< 0.1%
-74.54113 2
 
< 0.1%
-77.292665 2
 
< 0.1%
-86.260893 2
 
< 0.1%
Other values (90672) 90747
> 99.9%
ValueCountFrequency (%)
-166.661968 1
< 0.1%
-166.65656 1
< 0.1%
-166.654993 1
< 0.1%
-166.651656 1
< 0.1%
-166.628201 1
< 0.1%
ValueCountFrequency (%)
-66.962913 1
< 0.1%
-66.988551 1
< 0.1%
-66.996552 1
< 0.1%
-66.99675 1
< 0.1%
-67.010222 1
< 0.1%

is_fraud
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005784040455
Minimum0
Maximum1
Zeros90242
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:44.348014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.07583303164
Coefficient of variation (CV)13.11073673
Kurtosis167.9046567
Mean0.005784040455
Median Absolute Deviation (MAD)0
Skewness13.03460613
Sum525
Variance0.005750648687
MonotonicityNot monotonic
2025-06-23T18:29:44.369275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 90242
99.4%
1 525
 
0.6%
ValueCountFrequency (%)
0 90242
99.4%
1 525
 
0.6%
ValueCountFrequency (%)
1 525
 
0.6%
0 90242
99.4%

merch_zipcode
Real number (ℝ)

Missing 

Distinct22188
Distinct (%)28.8%
Missing13714
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean46668.58156
Minimum1003
Maximum99403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:44.397400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile7659
Q124916
median45694
Q368061
95-th percentile92632.8
Maximum99403
Range98400
Interquartile range (IQR)43145

Descriptive statistics

Standard deviation25843.17083
Coefficient of variation (CV)0.5537595094
Kurtosis-0.9974583605
Mean46668.58156
Median Absolute Deviation (MAD)21473
Skewness0.1542444807
Sum3595954215
Variance667869478.5
MonotonicityNot monotonic
2025-06-23T18:29:44.430752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34266 36
 
< 0.1%
21661 30
 
< 0.1%
16353 27
 
< 0.1%
33471 25
 
< 0.1%
16239 25
 
< 0.1%
44004 24
 
< 0.1%
43436 23
 
< 0.1%
79227 23
 
< 0.1%
47448 23
 
< 0.1%
33935 23
 
< 0.1%
Other values (22178) 76794
84.6%
(Missing) 13714
 
15.1%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1005 5
< 0.1%
1007 8
< 0.1%
1008 3
 
< 0.1%
1011 5
< 0.1%
ValueCountFrequency (%)
99403 2
 
< 0.1%
99402 2
 
< 0.1%
99401 2
 
< 0.1%
99371 7
< 0.1%
99362 4
< 0.1%
Distinct90667
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:44.559006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1724573
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90567 ?
Unique (%)99.8%

Sample

1st row2019-05-04 11:57:04
2nd row2019-12-14 08:55:21
3rd row2019-03-30 05:21:33
4th row2019-09-19 07:09:46
5th row2019-02-04 20:37:44
ValueCountFrequency (%)
2019-12-08 488
 
0.3%
2019-12-29 450
 
0.2%
2019-12-01 448
 
0.2%
2019-12-15 447
 
0.2%
2019-12-22 441
 
0.2%
2019-12-16 434
 
0.2%
2019-12-09 428
 
0.2%
2019-12-23 422
 
0.2%
2019-12-30 421
 
0.2%
2019-12-28 420
 
0.2%
Other values (55750) 177135
97.6%
2025-06-23T18:29:44.707558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 317148
18.4%
2 250464
14.5%
1 239192
13.9%
- 181534
10.5%
: 181534
10.5%
9 104273
 
6.0%
90767
 
5.3%
3 84286
 
4.9%
5 75187
 
4.4%
4 73887
 
4.3%
Other values (3) 126301
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1724573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 317148
18.4%
2 250464
14.5%
1 239192
13.9%
- 181534
10.5%
: 181534
10.5%
9 104273
 
6.0%
90767
 
5.3%
3 84286
 
4.9%
5 75187
 
4.4%
4 73887
 
4.3%
Other values (3) 126301
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1724573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 317148
18.4%
2 250464
14.5%
1 239192
13.9%
- 181534
10.5%
: 181534
10.5%
9 104273
 
6.0%
90767
 
5.3%
3 84286
 
4.9%
5 75187
 
4.4%
4 73887
 
4.3%
Other values (3) 126301
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1724573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 317148
18.4%
2 250464
14.5%
1 239192
13.9%
- 181534
10.5%
: 181534
10.5%
9 104273
 
6.0%
90767
 
5.3%
3 84286
 
4.9%
5 75187
 
4.4%
4 73887
 
4.3%
Other values (3) 126301
 
7.3%

age
Real number (ℝ)

Distinct83
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.56302401
Minimum13
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.2 KiB
2025-06-23T18:29:44.747589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile21
Q132
median44
Q357
95-th percentile79
Maximum95
Range82
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.40714748
Coefficient of variation (CV)0.3820454822
Kurtosis-0.1791284442
Mean45.56302401
Median Absolute Deviation (MAD)12
Skewness0.6036856753
Sum4135619
Variance303.0087833
MonotonicityNot monotonic
2025-06-23T18:29:44.779287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 2894
 
3.2%
34 2596
 
2.9%
35 2526
 
2.8%
46 2469
 
2.7%
43 2395
 
2.6%
44 2369
 
2.6%
32 2301
 
2.5%
33 2273
 
2.5%
31 2162
 
2.4%
45 2072
 
2.3%
Other values (73) 66710
73.5%
ValueCountFrequency (%)
13 5
 
< 0.1%
14 294
0.3%
15 436
0.5%
16 223
0.2%
17 170
 
0.2%
ValueCountFrequency (%)
95 20
 
< 0.1%
94 26
 
< 0.1%
93 286
0.3%
92 336
0.4%
91 369
0.4%